TY - JOUR
T1 - Energy analysis and optimization of a small-scale axial flow turbine for Organic Rankine Cycle application
AU - Engineer, Yohan
AU - Rezk, Ahmed
AU - Hossain, A K
N1 - ©2021 Published by Elsevier Ltd.This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/)
PY - 2021/11
Y1 - 2021/11
N2 - Increasing the cycle efficiency of Organic Rankine Cycles is an important R&D area. In this study, an effort has been made to optimize various parameters related to the axial flow turbine to maximize an ORC's efficiency. First, a numerical model for a small-scale single-stage axial flow turbine was developed and coupled with a 1D model of an existing ORC system. Then, a parametric study was undertaken for the system working under various turbine inlet conditions, such as turbine pressure ratios and working fluids. An optimization study was undertaken for the turbine flow profile using a low computational intensity Artificial Neural Network coupled with Genetic Algorithm optimization. Investigating the turbine losses revealed that the Mach Number is the most influential factor, which depends on the molar mass of the working fluid. Our study revealed that increasing the degree of superheat by up to 200% enhanced the turbine and overall cycle efficiency by 11% and 5%, respectively. Increasing the turbine total-to-static pressure ratio from 3 to 10 improved the turbine and cycle efficiency by up to 41% and 15%, respectively. Optimizing the turbine's flow profile enhanced the overall loss coefficient by 13.7%, the turbine's total-to-static efficiency by 5.2%, and the overall cycle efficiency from 8.78% to 9.02%.
AB - Increasing the cycle efficiency of Organic Rankine Cycles is an important R&D area. In this study, an effort has been made to optimize various parameters related to the axial flow turbine to maximize an ORC's efficiency. First, a numerical model for a small-scale single-stage axial flow turbine was developed and coupled with a 1D model of an existing ORC system. Then, a parametric study was undertaken for the system working under various turbine inlet conditions, such as turbine pressure ratios and working fluids. An optimization study was undertaken for the turbine flow profile using a low computational intensity Artificial Neural Network coupled with Genetic Algorithm optimization. Investigating the turbine losses revealed that the Mach Number is the most influential factor, which depends on the molar mass of the working fluid. Our study revealed that increasing the degree of superheat by up to 200% enhanced the turbine and overall cycle efficiency by 11% and 5%, respectively. Increasing the turbine total-to-static pressure ratio from 3 to 10 improved the turbine and cycle efficiency by up to 41% and 15%, respectively. Optimizing the turbine's flow profile enhanced the overall loss coefficient by 13.7%, the turbine's total-to-static efficiency by 5.2%, and the overall cycle efficiency from 8.78% to 9.02%.
KW - Energy Conversion
KW - ORC system
KW - Optimization
KW - Turbine Design
KW - Turbine Performance
UR - https://www.sciencedirect.com/science/article/pii/S2666202721000574
UR - http://www.scopus.com/inward/record.url?scp=85118842155&partnerID=8YFLogxK
U2 - 10.1016/j.ijft.2021.100119
DO - 10.1016/j.ijft.2021.100119
M3 - Article
SN - 2666-2027
VL - 12
JO - International Journal of Thermofluids
JF - International Journal of Thermofluids
M1 - 100119
ER -